





"""simple docstring"""





import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
       from scipy.stats import pearsonr, spearmanr
       from sklearn.metrics import fa_score, matthews_corrcoef


SCREAMING_SNAKE_CASE__   =  (
    "This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate "
    "library. You can have a look at this example script for pointers: "
    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"
)


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Dict				,		SCREAMING_SNAKE_CASE	:		Optional[int]     ):







      '''simple docstring'''




      warnings.warn(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
      requires_backends(SCREAMING_SNAKE_CASE				,		"""sklearn"""     )
      return (preds == labels).mean()


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Tuple				,		SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      warnings.warn(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
      requires_backends(SCREAMING_SNAKE_CASE				,		"""sklearn"""     )
      lowerCAmelCase    		=			simple_accuracy(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			fa_score(y_true=SCREAMING_SNAKE_CASE				,		y_pred=SCREAMING_SNAKE_CASE     )
      return {
          "acc": acc,
          "f1": fa,
          "acc_and_f1": (acc + fa) / 2,
      }


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[int]				,		SCREAMING_SNAKE_CASE	:		Dict     ):







      '''simple docstring'''




      warnings.warn(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
      requires_backends(SCREAMING_SNAKE_CASE				,		"""sklearn"""     )
      lowerCAmelCase    		=			pearsonr(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )[0]
      lowerCAmelCase    		=			spearmanr(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )[0]
      return {
          "pearson": pearson_corr,
          "spearmanr": spearman_corr,
          "corr": (pearson_corr + spearman_corr) / 2,
      }


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Dict				,		SCREAMING_SNAKE_CASE	:		Optional[int]				,		SCREAMING_SNAKE_CASE	:		Dict     ):







      '''simple docstring'''




      warnings.warn(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
      requires_backends(SCREAMING_SNAKE_CASE				,		"""sklearn"""     )
      assert len(SCREAMING_SNAKE_CASE     ) == len(SCREAMING_SNAKE_CASE     ), F'Predictions and labels have mismatched lengths {len(SCREAMING_SNAKE_CASE     )} and {len(SCREAMING_SNAKE_CASE     )}'
      if task_name == "cola":
            return {"mcc": matthews_corrcoef(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )}
      elif task_name == "sst-2":
            return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )}
      elif task_name == "mrpc":
            return acc_and_fa(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
      elif task_name == "sts-b":
            return pearson_and_spearman(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
      elif task_name == "qqp":
            return acc_and_fa(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
      elif task_name == "mnli":
            return {"mnli/acc": simple_accuracy(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )}
      elif task_name == "mnli-mm":
            return {"mnli-mm/acc": simple_accuracy(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )}
      elif task_name == "qnli":
            return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )}
      elif task_name == "rte":
            return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )}
      elif task_name == "wnli":
            return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )}
      elif task_name == "hans":
            return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )}
      else:
            raise KeyError(SCREAMING_SNAKE_CASE     )






def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Any				,		SCREAMING_SNAKE_CASE	:		Dict				,		SCREAMING_SNAKE_CASE	:		Optional[int]     ):







      '''simple docstring'''




      warnings.warn(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
      requires_backends(SCREAMING_SNAKE_CASE				,		"""sklearn"""     )
      if len(SCREAMING_SNAKE_CASE     ) != len(SCREAMING_SNAKE_CASE     ):
            raise ValueError(F'Predictions and labels have mismatched lengths {len(SCREAMING_SNAKE_CASE     )} and {len(SCREAMING_SNAKE_CASE     )}'     )
      if task_name == "xnli":
            return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )}
      else:
            raise KeyError(SCREAMING_SNAKE_CASE     )













"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {
    "facebook/s2t-wav2vec2-large-en-de": (
        "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = 'speech_to_text_2'
      _SCREAMING_SNAKE_CASE							      = ['past_key_values']
      _SCREAMING_SNAKE_CASE							      = {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}





      def __init__(			self ,		lowercase=10_000 ,		lowercase=6 ,		lowercase=2_048 ,		lowercase=4 ,		lowercase=0.0 ,		lowercase=True ,		lowercase="relu" ,		lowercase=256 ,		lowercase=0.1 ,		lowercase=0.0 ,		lowercase=0.0 ,		lowercase=0.02 ,		lowercase=2 ,		lowercase=True ,		lowercase=1 ,		lowercase=0 ,		lowercase=2 ,		lowercase=1_024 ,		**lowercase ,		)						->	Optional[int]:
            lowerCAmelCase    		=			vocab_size
            lowerCAmelCase    		=			d_model
            lowerCAmelCase    		=			decoder_ffn_dim
            lowerCAmelCase    		=			decoder_layers
            lowerCAmelCase    		=			decoder_attention_heads
            lowerCAmelCase    		=			dropout
            lowerCAmelCase    		=			attention_dropout
            lowerCAmelCase    		=			activation_dropout
            lowerCAmelCase    		=			activation_function
            lowerCAmelCase    		=			init_std
            lowerCAmelCase    		=			decoder_layerdrop
            lowerCAmelCase    		=			use_cache
            lowerCAmelCase    		=			decoder_layers
            lowerCAmelCase    		=			scale_embedding  # scale factor will be sqrt(d_model) if True
            lowerCAmelCase    		=			max_target_positions

            super().__init__(
                pad_token_id=lowercase ,		bos_token_id=lowercase ,		eos_token_id=lowercase ,		decoder_start_token_id=lowercase ,		**lowercase ,		)






